Reconstruction of Total Solar Irradiance by Deep Learning

Yasser Abduallah1, Jason T. L. Wang1, Yucong Shen1, Khalid A. Alobaid1, Serena Criscuoli2, Haimin Wang1

1. New Jersey Institute of Technology, University Heights, Newark, New Jersey, USA
2. National Solar Observatory, Boulder, Colorado, USA

Abstract

The Earth's primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation is measured. A minor change in the solar irradiance can have a significant impact on the Earth's climate and atmosphere. As a result, studying and measuring solar irradiance is crucial in understanding climate changes and solar variability. Several methods have been developed to reconstruct total solar irradiance for long and short periods of time; however, they are physics-based and rely on the availability of data, which does not go beyond 9,000 years. In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability. On the data that are available, our method agrees well with the state-of-the-art physics-based reconstruction models. To our knowledge, this is the first time that deep learning has been used to reconstruct total solar irradiance for more than 9,000 years.

Datasets and Source Code

ยป Click here to download the datasets and source code of the deep learning algorithm described in the paper.

Reference

Reconstruction of Total Solar Irradiance by Deep Learning, Y. Abduallah, J. T. L. Wang, Y. Shen, K. A. Alobaid, S. Criscuoli and H. Wang, Proceedings of the 34th International Florida Artificial Intelligence Research Society Conference (FLAIRS-34), North Miami Beach, Florida, USA, May 2021   [GitHub]